Efficient Color Image Segmentation Using Fastmap Algorithm

Kabilan, R.; Devaraj, G. Prince; Muthuraman, U.; Muthukumaran, N.; Gabriel, J. Zahariya; Swetha, R.

doi:10.1109/icicv50876.2021.9388470

Cited by 58 publications

(2 citation statements)

References 6 publications

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“…They selected the architecture of this DNN by combining different models [86][87][88][89][90]. Kabilan et al [91] used the fastmap method for colour-based image segmentation, where the results of fastmap and PCA were combined and optimised in an iterative process. Their method incorporated the Sobel gradient, median filter, and Gaussian smoothing.…”

Section: State Of the Artmentioning

confidence: 99%

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Alavigharahbagh,

Hajihashemi,

Machado

et al. 2023

Information

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In this article, a hierarchical method for action recognition based on temporal and spatial features is proposed. In current HAR methods, camera movement, sensor movement, sudden scene changes, and scene movement can increase motion feature errors and decrease accuracy. Another important aspect to take into account in a HAR method is the required computational cost. The proposed method provides a preprocessing step to address these challenges. As a preprocessing step, the method uses optical flow to detect camera movements and shots in input video image sequences. In the temporal processing block, the optical flow technique is combined with the absolute value of frame differences to obtain a time saliency map. The detection of shots, cancellation of camera movement, and the building of a time saliency map minimise movement detection errors. The time saliency map is then passed to the spatial processing block to segment the moving persons and/or objects in the scene. Because the search region for spatial processing is limited based on the temporal processing results, the computations in the spatial domain are drastically reduced. In the spatial processing block, the scene foreground is extracted in three steps: silhouette extraction, active contour segmentation, and colour segmentation. Key points are selected at the borders of the segmented foreground. The last used features are the intensity and angle of the optical flow of detected key points. Using key point features for action detection reduces the computational cost of the classification step and the required training time. Finally, the features are submitted to a Recurrent Neural Network (RNN) to recognise the involved action. The proposed method was tested using four well-known action datasets: KTH, Weizmann, HMDB51, and UCF101 datasets and its efficiency was evaluated. Since the proposed approach segments salient objects based on motion, edges, and colour features, it can be added as a preprocessing step to most current HAR systems to improve performance.

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Section: State Of the Artmentioning

confidence: 99%

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Alavigharahbagh,

Hajihashemi,

Machado

et al. 2023

Information

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“…Mask2Former extracts localized features by constraining cross-attention within predicted mask regions. Kabilan et al [36] improved segmentation accuracy and reduced complexity by using a three-step segmentation process involving the analysis of key components, mapping of similar objects in a faster way, and segmenting similar areas through color mapping.…”

Section: Image Segmentationmentioning

confidence: 99%

A Hybrid Image Segmentation Method for Accurate Measurement of Urban Environments

Kim

Lee

2023

Electronics

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In the field of urban environment analysis research, image segmentation technology that groups important objects in the urban landscape image in pixel units has been the subject of increased attention. However, since a dataset consisting of a huge amount of image and label pairs is required to utilize this technology, in most cases, a model trained with a dataset having similar characteristics is used for analysis, and as a result, the quality of segmentation is poor. To overcome this limitation, we propose a hybrid model to leverage the strengths of each model in predicting specific classes. In particular, we first introduce a pre-processing operation to reduce the differences between the collected urban dataset and public dataset. Subsequently, we train several segmentation models with a pre-processed dataset then, based on the weight rule, the segmentation results are fused to create one segmentation map. To evaluate our proposal, we collected Google Street View images that do not have any labels and trained a model using the cityscapes dataset which contains foregrounds similar to the collected images. We quantitatively assessed its performance using the cityscapes dataset with ground truths and qualitatively evaluated the results of GSV data segmentation through user studies. Our approach outperformed existing methods and demonstrated the potential for accurate and efficient urban environment analysis using computer vision technology.

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Detection and Automated Classification of Brain Tumor Types in MRI Images using Convolutional Neural Network with Grid Search Optimization

Devi¹,

Samundeeswari²

2021

2021 Fifth International Conference on I-Smac (IoT in Social, Mobile, Analytics and Cloud) (I-Smac)

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Efficient Color Image Segmentation Using Fastmap Algorithm

Cited by 58 publications

References 6 publications

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

Deep Learning Approach for Human Action Recognition Using a Time Saliency Map Based on Motion Features Considering Camera Movement and Shot in Video Image Sequences

A Hybrid Image Segmentation Method for Accurate Measurement of Urban Environments

Detection and Automated Classification of Brain Tumor Types in MRI Images using Convolutional Neural Network with Grid Search Optimization

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